The present invention relates to time-series data selection and, more specifically, to time-series data selection for use in detecting anomalies in information technology (IT) operations analytics.
When analytics systems focusing on analyses of time-series data are deployed in an IT environment, an early step in the deployment and configuration of such systems is the selection of a subset of data that will work well or be suitable for a given type of analysis from the available data. For example, when an analytic tool to retrieve and analyze data from a IT performance management system is deployed, the administrator or user responsible for the deployment may be required to explicitly select which tables of data to export (if they are contained within a database) or to specify which data to export.
In general though, what works well and provides the best possible results in one situation versus another situation is highly dependent on the actual analytic algorithms that are deployed but the administrators or users that are charged with the deployment and the configurations may have no real sense of what data should be selected. Thus a common approach to system deployment and configuration is based simply upon the notion of “best practices” in which previous experience and heuristics are codified in documentation which provide recommendations on which metrics to process. A variation on this theme is one where code and configurations to extract data are organized in deployable packages/packs that can be deployed together.